Lerner Research Institute News

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New Model May Predict Immunotherapy Response in Melanoma Patients

Dr. Gastman’s team found that high levels of a CD8 T-cell subpopulation may predict immune checkpoint inhibitor therapy resistance in melanoma.


A research team led by Brian Gastman, MD, staff in the Center for Immunotherapy & Precision Immuno-Oncology, has developed a new model that may aid in the prediction of which melanoma patients will respond to immune checkpoint inhibitor (ICI) therapy, a type of immunotherapy that stops “checkpoint” proteins from turning off the immune response against cancer cells.

According to study findings published in the Journal of Experimental Medicine, the model is capable of distinguishing ICI responders from non-responders with 88% accuracy by analyzing either tumor or peripheral blood CD8 T cells, a type of tumor-infiltrating immune cell that moves from the blood into a tumor and can destroy cancer cells.

“This model may become a focused tool to help clinicians predict who and who will not respond to standard-of-care immunotherapies,” said Dr. Gastman, who directs Cleveland Clinic’s Melanoma & High-Risk Skin Cancer Program. “This allows us to pivot treatment to the growing myriad of options being introduced to the field. Further studies will reveal whether this model can be used dynamically to reflect the changes in patients as they may develop acquired resistance, increasing its utility.”

Developing the new model

Since previous studies have established that CD8 T cells express high levels of ICI receptors and that CD8 T cell tumor infiltration correlates highly with immunotherapy response across different cancer types, Dr. Gastman’s team hypothesized that unique subpopulations of CD8 T cells may exist in both the tumor and peripheral blood of patients with melanoma.

Therefore, they performed a detailed characterization of CD8 T cells from the tumors and peripheral blood of eight patients with advanced melanoma and employed single-cell transcriptomic analysis to cluster different cell types. Their analysis revealed a unique subpopulation of CD8 T cells that they identified as CD8+ TOXPHOS cells.

“We were able to figure out that these CD8 T cells not only exist in the tumor, but that they also exist at a higher level in the blood of patients who were immunotherapy resistant,” said Dr. Gastman.“We then validated that finding against a number of patient blood samples.”

The researchers then developed a predictive model of ICI therapy response using a transcriptomic profile of CD8+ TOXPHOS cells. The model successfully predicted the ICI response in 11 of 12 patients from a training data set, resulting in a 92% predictive accuracy. It was validated further using three additional data sets, with an overall predictive accuracy of 88%.

From therapeutic resistance to therapeutic targeting

The results of this study could have vast implications— not only in predicting ICI resistance, but also in the potential therapeutic targeting of CD8+ TOXPHOS cells.

“We discovered multiple potential targetable pathways that are relatively unique to these cells, so we want to leverage their uniqueness to go right after them therapeutically,” said Dr. Gastman. “If we can understand the mechanisms that make these cells tick, we can go directly after them to change the course of immunotherapy.”

Several important questions remain to be answered on this path of discovery, including whether these cells cause resistance without any therapy, get generated after ICI treatment or are part of the resistance mechanism after a patient loses their response. 

Story adapted from ConsultQD.

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